In power bi how many values a slicer and hierarchy slicer visual can hold.Recently in one of the dashboard i used a hierarchy slicer for one field , where field contains 169k distinct values. Problem is slicer is not show all values and performance wise it is slow
Thanks,
B
Assuming you're talking about this visual: "Hierarchy Slicer" from App Source
https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380820?mktcmpid=Pickitli&tab=Overview
Understand that 'number of rows/items' is rarely a specified limit in PowerBI. The limiting factor is the memory of the machine running the report. At some point, things just slow down past usability.
Related
I have to divide two measures from two different tables. I have created a measure in Table A & created measure-2 in Table B.
When I use matrix visual in Power BI by taking date field in columns and region in rows (for table A&B), I can see the both table values are correct as I am expected.
Ex: Table A 2017-Q1 value by measure1 is 29.2, Table B 2017-Q1 value by measure1 is 2.9.
I have to divide both measures and I need to show the value (divide%) in TableA along with Measure1.
Unfortunately I tried in multiple ways by forming relationship b/w two tables also, But not getting the expected result i.e., 29.2/2.9 we should get 10% but instead of that getting 3%.
Without knowing your data model, it's hard to give a reasonable answer.
https://learn.microsoft.com/en-us/dax/related-function-dax
Your best change of understanding what happens is to learn up on relations, and changes them when needed. The documentation is a great starting point.
Unrelated data plotted in a visual of different data will always aggregate since there is no relation to split your values. The value of 3% is correct, your assumption that you want 10% as an outcome is not valid for your situation.
If you link the dates of table A and the dates of table B to a seperate Calendar, it all would work.
I'm pretty new to Power BI. I'm unsure how to approach this.
I have one visualization that displays the ten most frequently bought products in a time frame that is set by a slicer. In another visualization, I display how those products have been selling over the past few years (this time frame is not determined by the slicer). I want to display only the ten products that come from the first visualization, not the ten most common over the time frame in the second visualization.
How can I accomplish this? The approach I have in mind (and I'm open to others) is to create a true/false column that changes with the first visualization. "True" would be for products that are frequently bought as determined by the first visualization in the slicer-determined time range, and the second visualization would only look at values with a "true" in that column. How can I create a column (or table, maybe?) that changes depending on a visualization?
Clarification: most of the pages will say Top10 ... Actually, the measure used was a simple Top5 that includes products with the same number of orders than the 5th product. Therefore, to avoid dealing with larger images, 7 products will be seen but it is a Top5 ranking. The idea is you can replace it with your custom TopN measure.
What I understood:
The simplification of your model plus the disconnected help table would be:
I have one visualization that displays the ten most frequently bought
products in a time frame that is set by a slicer.
The Date slicer belongs to the Dates table in the Data model.
The table viz represents the number of rows in the sales table in the
current context (for each product within the Date range).
The table viz is sorted according to the [#Rows] measure in descending
order.
The table viz only presents the TopN products even without the presence
of the [#Rows] measure due to the presence of the [TopOrders]
measure within Filters on this visual. [TopOrders] is 1.
On the second page you create:
A slicer with the Dates[Date] column (the same one used on the
previous page).
A matrix with Products[ProductName] on the rows, HDates[Year] on
the columns, and a measure on values.
From the View tab, you select the Sync Slicers option.
Inside the Sync Slicers pane:
In the Sync column, check the boxes related to the necessary pages.
In the Display column uncheck the box that contains the over
years report.
So far all we have done is pass the time frame context from page 1 to page 2.
Since the TopN context depends on the time frame context, we can now use the [TopOrders] measure as a Filters on this visual in the matrix. Again, [TopOrders] is 1.
Why do the numbers differ between rows and not between columns?
Also, in this example, the Sales table only has information up to 12/31/2020 but the visualization shows an additional year and the Sales[Amount] values for each order is $1 so that [#Orders] and [SalesAmount] are the same for easy comparison.
HDates is not related to the model and for each combination of HDates[Year]-Products[ProductName], the [SalesAmount] measure is using the information coming from the previously hidden slicer and the respective Products[ProductName] because the information coming from HDates[Year] has no effect yet.
In order to complete this exercise, it only remains to modify the [SalesAmount] measure in such a way that it removes the filter on the time frame (Dates[Date]) and it recognizes HDates[Year] as Dates[Year].
SalesAmount :=
CALCULATE(
SUM(Sales[Amount]),
ALL(Dates),
TREATAS(VALUES(HDates[Year]),Dates[Year])
)
And this is the final result.
I hope it works for someone or the idea can be improved.
I am new to Power BI and with the limited time given, I am stuck at how to come up with:
Below Table B-Row1 ("1/20" and "M"-Monday cell) - how to
specifically place the date measures in their specific cell and put
it in one column?
How can I merge the cells under the Total column?
How to add all the numbers from the Type1 and Type2 columns and place it in the merged cell in #2?
Any clues/direction/links on how to achieve the Target Table B below will be much appreciated.
PS. Below Table A. Current is just using Matrix Visualization in Power BI.
You can't exactly do what you are after. PowerBI allows you to rapidly put amazing visuals together however that comes at the price of lack of (easy) flexibility. You could build your own custom visual or look in App Source for a visual that does this, or build the Visual in some other tool (via custom code).
However, I'd recommend sticking with the PowerBI matrix, which will give you a cascading drill down and work out how best to align your data to it and other out of the box visuals. Once you start to delve in to convoluted work-arounds to give users data in exactly the format they request you start to burn a lot of time. Look for alternatives to tell the data's story and work with your end-user to buy in to it.
Just wanna share that I have resolved my problem not using one type of visualization, but through using 3 different visualizations in Power BI. I used:
1 Table visual for Date column
1 Table visual for Total column
1 Matrix visual for the Code+Type mapping and counts
I also used DAX function to get the Date format and another DAX function used for both Total and Code+Type counts(to filter data according to the specified date).
Thanks for the response, #Murray and #RADO.
I have the following scenario.
I have two fact tables [FactA and FactB]. FactA and FactB contains 9 common dimensions and their own measures. I am trying to pull the measures from both fact tables into a table visualization and trying to filter the results using common dimensions. It works for the measure pulled from one fact table and not for the other fact table. I tried to set the cross filter direction to both on all the relationships between common dimensions and facts. I was able to set the cross filter for one dimension table, when I am trying to set the same for remaining dimensions, I am getting an warning that Power BI desktop allows only one filtering path between tables in a data model.
Also I tried to see if the cross filtering is working fine for the one dimension that I enabled cross filtering by pulling that dimension key and measures from both facts. But it didn't change anything.
Is there a way I can pull measures from both facts and have it filtered by common dimension values? Please share your thoughts and suggestion. Thanks in advance.
I have joined all the dimension tables to both fact tables. All dimension tables have 1 to many relationships with Fact tables. I have hidden all the dimension columns in report view for both fact tables. I just have measures visible on both Facts. In the visualization tab, I have pulled the measures from fact tables and dimension columns from dimension tables. It fixed the issue.
Also I learnt that, you can derive a new measure using other measures available in different fact tables.
I have created a powerpivot model include in the image below. I am trying to include the "IncurredLoss" value and have it sliced by time. Written Premium is in the fact table and is displaying correctly. I am aiming for IncurredLoss to display in a similar fashion
I have tried the following solutions:
Add new related column: Related(LossSummary[IncurredLoss]). Result: No data
DAX Summary Measure: =CALCULATE(SUM(LossSummary[IncurredLoss])). Result: Sum of everything in LossSummary[IncurredLoss] (not time sliced)
Simply adding the Incurred Loss column to the Pivot Table panel. Result: Sum of everything in LossSummary[IncurredLoss] (not time sliced)
A few other notes:
LossKey joins LossSummary to PolicyPremiumFact
Reportdate joins PolicyPremiumFact to the Calendar.
There is 1 row in LossSummary per date and Policy. LossKey contains this information and is the PK on that table.
Any ideas, clarifications or pointers are most certainly welcome. Thank you!
The related column should work. I was able to get it to work in both Excel 2016 and Power BI Desktop. Rather than bombarding you with questions, I'll try and walk through how I would troubleshoot further, in the hopes it gets you to a solution faster:
First, check the PolicyPremiumFact table inside Power Pivot and see if the IncurredLossRelated field is blank or not. If it is consistently blank, then the related column isn't working. The primary reason the related column wouldn't work is if there's a problem with your relationships. Things I would check:
Ensure that the relationships are between the fields you think they are between (i.e. you didn't accidentally join LossKey in one table to a different field in the other table)
Ensure that the joined fields contain the same data (i.e. you didn't call a field LossKey, but in fact, it isn't the LossKey at all)
Ensure that the joined fields are the same data type in Power Pivot (this is most common with dates: e.g. joining a text field that looks like a date to an actual date field may work, but not act as expected)
If none of the above are the problem, it doesn't hurt to walk through your data for a given date in Power Pivot. E.g. filter your PolicyPremiumFact table to a specific date and look at the LossKeys. Then go the LossSummary table and filter to those LossKeys. Stepping through like this might reveal an oversight (e.g. maybe the LossKeys weren't fully loaded into your model).
If none of the above reveals anything, or if the related column is not blank inside Power Pivot, my suggestion would be to try a newer version of Excel (e.g. Excel 2016), or the most recent version of Power BI Desktop.
If the issue still occurs in the most recent version of Excel/Power BI Desktop, then there's something else going on with your data model that's impacting the RELATED calculation. If that's the case, it would be very helpful if you could mock up your file with sample data that reproduces the problem and share it.
One final suggestion I have is to consider restructuring your tables before they arrive in your data model. In your case, I'd recommend restructuring PolicyPremiumFact to include all the facts from LossSummary, rather than having a separate table joined to your primary fact table. This is what you're doing with the RELATED field to some extent, but it's cleaner to do before or as your data is imported into Power Pivot (e.g. using SQL or Power Query) rather than in DAX.
Hope some of this helps.